Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/106772
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Applied Mathematics | en_US |
dc.creator | Lee, CY | en_US |
dc.creator | Wong, KY | en_US |
dc.date.accessioned | 2024-06-03T07:10:56Z | - |
dc.date.available | 2024-06-03T07:10:56Z | - |
dc.identifier.issn | 0962-2802 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/106772 | - |
dc.language.iso | en | en_US |
dc.publisher | Sage Publications Ltd. | en_US |
dc.rights | This is the accepted version of the publication Yin Lee C, Wong KY. Survival analysis with a random change-point. Statistical Methods in Medical Research. 2023;32(11):2083-2095. Copyright © 2023 (The Author(s)). DOI:10.1177/09622802231192946 | en_US |
dc.subject | Breast cancer | en_US |
dc.subject | Expectation–maximization algorithm | en_US |
dc.subject | Profile likelihood | en_US |
dc.subject | Proportional hazards model | en_US |
dc.subject | Right-censored data | en_US |
dc.title | Survival analysis with a random change-point | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 2083 | en_US |
dc.identifier.epage | 2095 | en_US |
dc.identifier.volume | 32 | en_US |
dc.identifier.issue | 11 | en_US |
dc.identifier.doi | 10.1177/09622802231192946 | en_US |
dcterms.abstract | Contemporary works in change-point survival models mainly focus on an unknown universal change-point shared by the whole study population. However, in some situations, the change-point is plausibly individual-specific, such as when it corresponds to the telomere length or menopausal age. Also, maximum-likelihood-based inference for the fixed change-point parameter is notoriously complicated. The asymptotic distribution of the maximum-likelihood estimator is non-standard, and computationally intensive bootstrap techniques are commonly used to retrieve its sampling distribution. This article is motivated by a breast cancer study, where the disease-free survival time of the patients is postulated to be regulated by the menopausal age, which is unobserved. As menopausal age varies across patients, a fixed change-point survival model may be inadequate. Therefore, we propose a novel proportional hazards model with a random change-point. We develop a nonparametric maximum-likelihood estimation approach and devise a stable expectation–maximization algorithm to compute the estimators. Because the model is regular, we employ conventional likelihood theory for inference based on the asymptotic normality of the Euclidean parameter estimators, and the variance of the asymptotic distribution can be consistently estimated by a profile-likelihood approach. A simulation study demonstrates the satisfactory finite-sample performance of the proposed methods, which yield small bias and proper coverage probabilities. The methods are applied to the motivating breast cancer study. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Statistical methods in medical research, Nov. 2023, v. 32, no. 11, p. 2083-2095 | en_US |
dcterms.isPartOf | Statistical methods in medical research | en_US |
dcterms.issued | 2023-11 | - |
dc.identifier.eissn | 1477-0334 | en_US |
dc.description.validate | 202312 bcch | en_US |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | a2537 | - |
dc.identifier.SubFormID | 47832 | - |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | Central Guided Local Science and Technology Development Funds for Research Laboratories | en_US |
dc.description.pubStatus | Published | en_US |
dc.description.oaCategory | Green (AAM) | en_US |
Appears in Collections: | Journal/Magazine Article |
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File | Description | Size | Format | |
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Lee_Survival_Random_Change-point.pdf | Pre-Published version | 466.95 kB | Adobe PDF | View/Open |
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